Before the 1970s, the phylogeny of the prokaryotes was based on crude comparisons of morphology and pattern of substrate utilization and was largely ignored due to the presumed simplicity of the organisms. Carl Woese used a different strategy to tackle prokaryotic phylogeny. He focused on sequence comparisons of the ribosome, a biomolecule found in all life forms. The ribosome is an essential macromolecule that is involved in the translation of messenger RNA into proteins. Woese argued that since protein synthesis is an essential function for life, the ribosome could not withstand major sequence changes or life would cease. He then targeted one molecule, the 16S rRNA of prokaryotes and the analogous 18S rRNA for eukaryotes, and did comparisons by sequence analysis (Woese, C. R. and G. E. Fox Proc. Natl. Acad. Sci. USA, 1977, 74:5088-5090). A new phylogeny of all life was discovered and to his surprise (and other biologists), the old phylogeny of eukaryotes and prokaryotes was discarded for a three-kingdom version that included bacteria, archaea, and eucarya (shown in FIG. 1). Over time, most biologists have accepted this paradigm shift. To date, 35 bacteria phyla and 18 archaea phyla were identified, despite only having 30 cultivatable representatives for both (Hugenholtz, P. Genome Biol, 2002, 3(2):0003). With the discovery of a robust bacterial phylogeny by Woese, molecular biology-based methods have slowly replaced traditional methods in the study of microbial populations in environmental samples (Woese, C. R. et al. Proc. Natl. Acad. Sci. USA, 1990, 87:4576-4579). These molecular biology based techniques rely on the 16S rRNA, the biomolecule used by Woese to determine the phylogeny of bacteria and archaea. Over the past twenty years, molecular biology tool development has progressed from determining community structure to community function.
Specific microbial populations have a unique sequence signature within the 16S or 18S rRNA. Norman Pace recognized that specific microbial populations have signature sequences within the 16S or 18S rRNA that can be targeted by molecular biology based methods. Pace's group was the first to demonstrate the use of fluorescence in situ hybridizations with an oligonucleotide probe that is complementary to these signature sequences (DeLong, E. F. et al. Science, 1989, 243(4896):1360-3). With this approach, they were able to identify and enumerate microbes within a mixed culture sample at various phylogenetic levels. Today, probes and their hybridization characteristics for specific microbial populations are commercially available through convenient websites (Loy, A. et al. Nucleic Acids Res, 2003, 31(1):514-516). For example, the sequence, hybridization conditions, and other characteristics of an oligonucleotide probe that targets the 16S rRNA of the genus Nitrospira (ProbeBase accession number pB-00627) are as follows: specificity: Nitrospira spp.; target molecule: 16S rRNA; position: 447-464; sequence: 5′-GGTTTCCCGTTCCATCTT-3′ (SEQ ID NO:1); length: 18 nt; G+C content: 50%; Tm: 48° C.; delta Gs: ΔG1: −22.03; ΔG2: 1.41; ΔG12: −21.96; MW: 5406 g/mol; formamide: 30%; (Schramm, A. et al. Appl. Environ. Microbiol., 1998, 64:3480-3485; information provided by ProbeBase, an online database of probes at the Department of Microbial Ecology, University of Vienna).
Molecular biology-based methods have now replaced classical methods in the study of microbes. Since Pace's demonstration of FISH, molecular biology-based methods have been developed to investigate microbial populations in mixed cultures, such as bioreactors and environmental samples. As shown by FIG. 2, three classes of molecular biology-based methods have been developed to identify, enumerate, and determine the function of specific microbial populations. A fourth class of molecular biology based methods provides a measure of the diversity. All of these molecular biology-based methods draw on the sequence information of the 16S rRNA.
The investigation of the microbiology of mixed culture samples involves determining the identity and abundance of microbes present (microbial community structure) and their role in the mixed culture sample (microbial community function). Traditionally, light microscopy or culture-based methods were used to characterize the microbial structure of mixed culture samples. More recently, new tools that draw on molecular biology and a new view of the phylogeny of life have been developed to identity bacteria and determine their function.
Molecular biology tools have been used to determine community structure and function. The first wave of molecular biology tools identify and enumerate specific microbial populations in environmental systems. Recently, Amann et al. (Amann, R. et al. FEMS Microbiology Ecology, 1998, 25:205-215) reviewed molecular biology based techniques for identifying and enumerating bacterial populations and these are summarized below. For specific microbial populations where the 16S rRNA sequence information is available, tools are available to identify individual cells in situ (fluorescence in situ hybridizations or FISH) (DeLong, E. F. et al. Science, 1989, 243(4896):1360-3) or provide estimates of abundance for a microbial population ex situ (membrane hybridizations). For uncharacterized samples, researchers use DNA amplification by polymerase chain reaction (PCR) that targets large phylogenetic groups combined with conventional cloning methods to identify the different types of microbes present. Finally, fingerprinting methods such as terminal restriction length polymorphism (T-RFLP) and denaturing gradient gel electrophoresis (DGGE) characterize the diversity and evenness of environmental samples (Liu, W. T. Water Science and Technology, 1998, 37(4-5): 417-422; Kaewpipat, K. and C. P. Grady, Jr. Water Sci Technol, 2002, 46(1-2):19-27; Kreuzinger, N. et al. Water Sci Technol, 2003, 47(11):165-72).
The second wave of molecular biology tools determined the function of specific microbial populations in situ or ex situ. FISH is combined with microautoradiography (FISH-MAR) to provide a method that identifies microbes that metabolize specific compounds. With FISH-MAR, environmental samples are exposed to radio-labeled substrates. In some cases, the rate of substrate uptake has been reported (Nielsen, J. L. et al. Environ Microbiol, 2003, 5(3):202-11). FISH-MAR is a difficult method to master, which limits its acceptance as a second wave tool. An ex situ method called Isotope Array is based on the same principle as FISH-MAR, but membrane hybridizations are used to identify the dominant microbial population linked to substrate uptake (Adamczyk, J. et al. Appl Environ Microbiol, 2003, 69(11):6875-87).
Molecular biology tools for examining the growth activity of microbial communities in environmental samples are being utilized. Three strategies are currently used for determining the growth activity of the microbial members in biological reactor systems. The simplest strategy involves detecting and enumerating the bacteria that are only able to carry out certain metabolic functions. In this case, a simple identification and enumeration by the methods used for microbial structure analysis are needed. The second strategy determines the abundance of genes or mRNA present in a sample that is specific for an enzyme in the specific metabolic pathway of interest. The identification of the microbes containing these genes or mRNA is not always possible, since these biomolecules are not phylogenetic markers and are present at low cellular levels. The third strategy determines whether the microbes of interest are growing. With this strategy, the measurement of the rRNA present in the cells is required. Membrane hybridizations have been used by researchers as evidence that a bacterial population is active when their relative 16S rRNA levels increase. Detection of increased ribosome synthesis has been used to determine when bacterial populations or individual cells of a bacterial population are actively growing. These methods and others involving genetically modified organisms have been reviewed (Molin, S, and M. Givskov Environmental Microbiology, 1999, 1(5):383-391).
For the past 50 years, scientists have been measuring the specific growth rate of pure cultures by using spectrophotometers (see FIGS. 3A and 3B). Over time, the optical density is measured for a defined wavelength and compared to a blank that contains sterile broth media. With a simple spreadsheet, the specific growth rate of the culture is determined by examination of the rate of increase of the optical density.
The specific rate of ribosome synthesis (or ribosome doubling time) is identical to the specific growth rate (or cell doubling time) of the culture. During log growth, cells are growing at a constant specific growth rate, which also means they have a defined and constant doubling time. Similarly, the ribosome doubling time has to be identical to the cell doubling time, which is depicted in FIG. 4.
During the 1960's, researchers first reported that the macromolecular composition of pure cultures was dependent on the growth rate (Maaløe, O. and N. O. Kjeldgaard, “Control of Macromolecular Synthesis; a study of DNA, RNA, and protein synthesis in bacteria” 1966, New York: W. A. Benjamin, p. 284). The relationship between the macromolecular composition and growth phase of E. coli strain B/r is shown in Table I (Bremer, H. and P. P. Dennis, “Modulation of chemical composition and other parameters of the cell by growth rate” in Escherichia coli and Salmonella, F. C. Neidhardt, et al., Editors; 1996, ASM Press: Washington, D.C.). Two basic descriptors of ribosome synthesis, rRNA transcription and cellular ribosome levels, are also included. The rRNA transcription is reported as the fraction of total transcription.
TABLE 1Comparison of specific growth rate, rRNA transcription,and macromolecular composition of E. coli strain B/r.Specific GrowthrRNARibosomesRatetranscriptionper cellComposition %hr−1%—RNADNAProtein0.6356,800145682.57372,00024252
An approximately 10-fold increase in ribosome level is observed when E. coli increases its specific growth rate from 0.6 hr−1 to 2.5 hr−1. During rapid growth, over 50% of the total RNA produced in E. coli is ribosomal RNA (rRNA), which is remarkable given that there are only 14 promoters associated with the seven rrn operons compared to 2,000 totalpromoters available (Gourse, R. L. and M. Nomura, “Prokaryotic rRNA gene expression, in Ribosomal RNA: structure, evolution, processing, and function in protein biosynthesis” R. A. Zimmermann and A. E. Dahlberg, Editors. 1996, CRC Press, Inc.: Boca Raton. p. 373-394). The largest macromolecule fraction for all growth rates is protein. As the growth rate increases, the RNA content increases and protein content decreases. This is caused by the increase of ribosome levels or stable RNA. Bremer and Dennis (Bremer, H. and P. P. Dennis, “Modulation of chemical composition and other parameters of the cell by growth rate” in Escherichia coli and Salmonella, F. C. Neidhardt, et al., Editors; 1996, ASM Press: Washington, D.C.) developed a growth equation for E. coli that was a function of constant ribosome concentration (number of ribosomes per protein) and activity (protein synthesis rate per ribosome).
Some researchers have used fluorescence in situ hybridizations with probes that target the ribosomes in cells and reported that faster growing cells have higher levels of ribosomes based on fluorescent intensity (DeLong, E. F. et al. Science, 1989, 243(4896):1360-3; Poulsen, L. K. et al. Appl Environ Microbiol, 1993, 59(5):1354-60). However, this approach was discarded as a method for measuring the specific growth rate (or cell doubling time), since cells maintain high levels of ribosomes during stationary phase which would be misinterpreted as rapidly growing cells.
Central to microbial growth is ribosome synthesis, the production of functional ribosomes. Currently, the ribosome synthesis model of Escherichia coli is the most complete, best understood, and hypothesized to describe ribosome synthesis for Bacteria. A basic review of E. coli ribosome synthesis is provided below, however several detailed reviews of E. coli ribosome synthesis are available (Gourse, R. L. and M. Nomura, “Prokaryotic rRNA gene expression, in Ribosomal RNA. structure, evolution, processing, and function in protein biosynthesis” R. A. Zimmermann and A. E. Dahlberg, Editors. 1996, CRC Press, Inc.: Boca Raton. p. 373-394; Jemiolo, D. K. “Processing of Prokaryotic ribosomal RNA” in Ribosomal RNA: structure, evolution, processing, and function in protein biosynthesis, R. A. Zimmermann and A. E. Dahlberg, Editors; 1996, CRC Press, Inc.: Boca Raton, p. 453-468; Srivastava, A. K. and D. Schlessinger Annual Review of Microbiology, 1990, 44:105-129). A schematic of ribosome synthesis in bacteria is shown in FIG. 5. Expression of the rrn operon produces a polycistronic transcript consisting of the three rRNAs: 5S, 16S, and 23S. Two processing steps are required to produce mature rRNAs for ribosome assembly. In the primary processing step, RNaseIII cleaves the polycistronic transcript resulting in three precursor rRNAs: precursor 5S (pre5S), precursor 16S (pre16S), and precursor 23S (pre23S). A secondary processing step removes unnecessary RNA from both 5′ and 3′ ends of the precursor rRNAs before ribosome assembly. This secondary processing step is slower than the primary processing step, which results in an intracellular pool of precursor rRNAs.
Chloramphenicol disrupts ribosome synthesis. As shown in FIG. 6 and FIG. 7, chloramphenicol inhibits the secondary processing of precursor 16S rRNA, but does not inhibit the production of precursor 16S rRNA (Tomlins, R. I. and Z. J. Ordal J Bacteriol, 1971, 107(1):134-42). Cangelosi and Brabant (Cangelosi, G. A. and W. H. Brabant Journal of Bacteriology, 1997, 179(14):4457-4463) used a reverse transcription method to measure the level of precursor 16S rRNA in cells of E. coli that were exposed to chloramphenicol. Their results suggested a marked difference in the rate of the buildup of the pre16S rRNA in growing and non-growing cells that were exposed to chloramphenicol. Chloramphenicol treated E. coli cells were also reported to have substantially higher level of pre16S rRNA than normally observed for LB cultures (Licht, T. R. et al. Environmental Microbiology, 1999, 1(1):23-32).
FIG. 7 is a simplified example of a cell in log growth phase that is exposed to chloramphenicol. In this figure, the initial level of pre16S rRNA is zero compared to the level of 16S rRNA (80,000), which represents ribosomes. After exposure to chloramphenicol, the level of 16S rRNA remains constant, while the pre16S rRNA increases to 40,000 after 15 minutes and 80,000 after 30 minutes. For non-growing cells (e.g., in stationary phase) exposed to chloramphenicol, the level of pre16S rRNA and 16S rRNA will remain constant.
U.S. Pat. Nos. 5,770,373; 5,726,021; and 5,712,095, which are each incorporated by reference herein in its entirety, describe methods for identifying chloramphenicol-resistant strains of mycobacteria, and the typical response of ribosome synthesis to chloramphenicol. U.S. Patent Application Publication No. 200400772242, which is incorporated herein by reference in its entirety, describes a method for detecting, enumerating and/or identifying microorganisms in a sample. U.S. Patent Application Publication No. 20060105339, which is incorporated herein by reference in its entirety, describes a method for measuring the rates of replication and death of microbial infectious agents within an infected host organism. A molecular biology-based method that measures the specific growth rate (or cell doubling time) of distinct microbial populations in a mixed culture has not previously been reported.
The identification of microbial populations through the use of molecular biology-based methods has been a boon for researchers in the areas of environmental science and engineering, microbial ecology, drug discovery, public health, homeland security, etc. A molecular biology-based tool that measures the specific growth rate of distinct microbial populations would be of great interest to scientists and engineers that share an interest in determining how fast microbes are growing. Industries that may benefit include, but are not limited to, environmental systems (water and wastewater treatment systems), bioremediation (optimization of conditions for microbial growth), public health (identification of rapidly growing infectious microbes), and homeland security (identification of rapidly growing bioterrorism agents).